LoessInterpolator.java

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package org.apache.commons.math4.legacy.analysis.interpolation;

import java.util.Arrays;

import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialSplineFunction;
import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
import org.apache.commons.math4.legacy.exception.NoDataException;
import org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException;
import org.apache.commons.math4.legacy.exception.NotFiniteNumberException;
import org.apache.commons.math4.legacy.exception.NotPositiveException;
import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException;
import org.apache.commons.math4.legacy.exception.OutOfRangeException;
import org.apache.commons.math4.legacy.exception.util.LocalizedFormats;
import org.apache.commons.math4.core.jdkmath.JdkMath;
import org.apache.commons.math4.legacy.core.MathArrays;

/**
 * Implements the <a href="http://en.wikipedia.org/wiki/Local_regression">
 * Local Regression Algorithm</a> (also Loess, Lowess) for interpolation of
 * real univariate functions.
 * <p>
 * For reference, see
 * <a href="http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1979.10481038">
 * William S. Cleveland - Robust Locally Weighted Regression and Smoothing
 * Scatterplots</a></p>
 * <p>
 * This class implements both the loess method and serves as an interpolation
 * adapter to it, allowing one to build a spline on the obtained loess fit.</p>
 *
 * @since 2.0
 */
public class LoessInterpolator
    implements UnivariateInterpolator {
    /** Default value of the bandwidth parameter. */
    public static final double DEFAULT_BANDWIDTH = 0.3;
    /** Default value of the number of robustness iterations. */
    public static final int DEFAULT_ROBUSTNESS_ITERS = 2;
    /**
     * Default value for accuracy.
     * @since 2.1
     */
    public static final double DEFAULT_ACCURACY = 1e-12;
    /**
     * The bandwidth parameter: when computing the loess fit at
     * a particular point, this fraction of source points closest
     * to the current point is taken into account for computing
     * a least-squares regression.
     * <p>
     * A sensible value is usually 0.25 to 0.5.</p>
     */
    private final double bandwidth;
    /**
     * The number of robustness iterations parameter: this many
     * robustness iterations are done.
     * <p>
     * A sensible value is usually 0 (just the initial fit without any
     * robustness iterations) to 4.</p>
     */
    private final int robustnessIters;
    /**
     * If the median residual at a certain robustness iteration
     * is less than this amount, no more iterations are done.
     */
    private final double accuracy;

    /**
     * Constructs a new {@link LoessInterpolator}
     * with a bandwidth of {@link #DEFAULT_BANDWIDTH},
     * {@link #DEFAULT_ROBUSTNESS_ITERS} robustness iterations
     * and an accuracy of {#link #DEFAULT_ACCURACY}.
     * See {@link #LoessInterpolator(double, int, double)} for an explanation of
     * the parameters.
     */
    public LoessInterpolator() {
        this.bandwidth = DEFAULT_BANDWIDTH;
        this.robustnessIters = DEFAULT_ROBUSTNESS_ITERS;
        this.accuracy = DEFAULT_ACCURACY;
    }

    /**
     * Construct a new {@link LoessInterpolator}
     * with given bandwidth and number of robustness iterations.
     * <p>
     * Calling this constructor is equivalent to calling {link {@link
     * #LoessInterpolator(double, int, double) LoessInterpolator(bandwidth,
     * robustnessIters, LoessInterpolator.DEFAULT_ACCURACY)}
     * </p>
     *
     * @param bandwidth  when computing the loess fit at
     * a particular point, this fraction of source points closest
     * to the current point is taken into account for computing
     * a least-squares regression.
     * A sensible value is usually 0.25 to 0.5, the default value is
     * {@link #DEFAULT_BANDWIDTH}.
     * @param robustnessIters This many robustness iterations are done.
     * A sensible value is usually 0 (just the initial fit without any
     * robustness iterations) to 4, the default value is
     * {@link #DEFAULT_ROBUSTNESS_ITERS}.

     * @see #LoessInterpolator(double, int, double)
     */
    public LoessInterpolator(double bandwidth, int robustnessIters) {
        this(bandwidth, robustnessIters, DEFAULT_ACCURACY);
    }

    /**
     * Construct a new {@link LoessInterpolator}
     * with given bandwidth, number of robustness iterations and accuracy.
     *
     * @param bandwidth  when computing the loess fit at
     * a particular point, this fraction of source points closest
     * to the current point is taken into account for computing
     * a least-squares regression.
     * A sensible value is usually 0.25 to 0.5, the default value is
     * {@link #DEFAULT_BANDWIDTH}.
     * @param robustnessIters This many robustness iterations are done.
     * A sensible value is usually 0 (just the initial fit without any
     * robustness iterations) to 4, the default value is
     * {@link #DEFAULT_ROBUSTNESS_ITERS}.
     * @param accuracy If the median residual at a certain robustness iteration
     * is less than this amount, no more iterations are done.
     * @throws OutOfRangeException if bandwidth does not lie in the interval [0,1].
     * @throws NotPositiveException if {@code robustnessIters} is negative.
     * @see #LoessInterpolator(double, int)
     * @since 2.1
     */
    public LoessInterpolator(double bandwidth, int robustnessIters, double accuracy)
        throws OutOfRangeException,
               NotPositiveException {
        if (bandwidth < 0 ||
            bandwidth > 1) {
            throw new OutOfRangeException(LocalizedFormats.BANDWIDTH, bandwidth, 0, 1);
        }
        this.bandwidth = bandwidth;
        if (robustnessIters < 0) {
            throw new NotPositiveException(LocalizedFormats.ROBUSTNESS_ITERATIONS, robustnessIters);
        }
        this.robustnessIters = robustnessIters;
        this.accuracy = accuracy;
    }

    /**
     * Compute an interpolating function by performing a loess fit
     * on the data at the original abscissae and then building a cubic spline
     * with a
     * {@link org.apache.commons.math4.legacy.analysis.interpolation.SplineInterpolator}
     * on the resulting fit.
     *
     * @param xval the arguments for the interpolation points
     * @param yval the values for the interpolation points
     * @return A cubic spline built upon a loess fit to the data at the original abscissae
     * @throws NonMonotonicSequenceException if {@code xval} not sorted in
     * strictly increasing order.
     * @throws DimensionMismatchException if {@code xval} and {@code yval} have
     * different sizes.
     * @throws NoDataException if {@code xval} or {@code yval} has zero size.
     * @throws NotFiniteNumberException if any of the arguments and values are
     * not finite real numbers.
     * @throws NumberIsTooSmallException if the bandwidth is too small to
     * accommodate the size of the input data (i.e. the bandwidth must be
     * larger than 2/n).
     */
    @Override
    public final PolynomialSplineFunction interpolate(final double[] xval,
                                                      final double[] yval)
        throws NonMonotonicSequenceException,
               DimensionMismatchException,
               NoDataException,
               NotFiniteNumberException,
               NumberIsTooSmallException {
        return new SplineInterpolator().interpolate(xval, smooth(xval, yval));
    }

    /**
     * Compute a weighted loess fit on the data at the original abscissae.
     *
     * @param xval Arguments for the interpolation points.
     * @param yval Values for the interpolation points.
     * @param weights point weights: coefficients by which the robustness weight
     * of a point is multiplied.
     * @return the values of the loess fit at corresponding original abscissae.
     * @throws NonMonotonicSequenceException if {@code xval} not sorted in
     * strictly increasing order.
     * @throws DimensionMismatchException if {@code xval} and {@code yval} have
     * different sizes.
     * @throws NoDataException if {@code xval} or {@code yval} has zero size.
     * @throws NotFiniteNumberException if any of the arguments and values are
     not finite real numbers.
     * @throws NumberIsTooSmallException if the bandwidth is too small to
     * accommodate the size of the input data (i.e. the bandwidth must be
     * larger than 2/n).
     * @since 2.1
     */
    public final double[] smooth(final double[] xval, final double[] yval,
                                 final double[] weights)
        throws NonMonotonicSequenceException,
               DimensionMismatchException,
               NoDataException,
               NotFiniteNumberException,
               NumberIsTooSmallException {
        if (xval.length != yval.length) {
            throw new DimensionMismatchException(xval.length, yval.length);
        }

        final int n = xval.length;

        if (n == 0) {
            throw new NoDataException();
        }

        NotFiniteNumberException.check(xval);
        NotFiniteNumberException.check(yval);
        NotFiniteNumberException.check(weights);

        MathArrays.checkOrder(xval);

        if (n == 1) {
            return new double[]{yval[0]};
        }

        if (n == 2) {
            return new double[]{yval[0], yval[1]};
        }

        int bandwidthInPoints = (int) (bandwidth * n);

        if (bandwidthInPoints < 2) {
            throw new NumberIsTooSmallException(LocalizedFormats.BANDWIDTH,
                                                bandwidthInPoints, 2, true);
        }

        final double[] res = new double[n];

        final double[] residuals = new double[n];
        final double[] sortedResiduals = new double[n];

        final double[] robustnessWeights = new double[n];

        // Do an initial fit and 'robustnessIters' robustness iterations.
        // This is equivalent to doing 'robustnessIters+1' robustness iterations
        // starting with all robustness weights set to 1.
        Arrays.fill(robustnessWeights, 1);

        for (int iter = 0; iter <= robustnessIters; ++iter) {
            final int[] bandwidthInterval = {0, bandwidthInPoints - 1};
            // At each x, compute a local weighted linear regression
            for (int i = 0; i < n; ++i) {
                final double x = xval[i];

                // Find out the interval of source points on which
                // a regression is to be made.
                if (i > 0) {
                    updateBandwidthInterval(xval, weights, i, bandwidthInterval);
                }

                final int ileft = bandwidthInterval[0];
                final int iright = bandwidthInterval[1];

                // Compute the point of the bandwidth interval that is
                // farthest from x
                final int edge;
                if (xval[i] - xval[ileft] > xval[iright] - xval[i]) {
                    edge = ileft;
                } else {
                    edge = iright;
                }

                // Compute a least-squares linear fit weighted by
                // the product of robustness weights and the tricube
                // weight function.
                // See http://en.wikipedia.org/wiki/Linear_regression
                // (section "Univariate linear case")
                // and http://en.wikipedia.org/wiki/Weighted_least_squares
                // (section "Weighted least squares")
                double sumWeights = 0;
                double sumX = 0;
                double sumXSquared = 0;
                double sumY = 0;
                double sumXY = 0;
                double denom = JdkMath.abs(1.0 / (xval[edge] - x));
                for (int k = ileft; k <= iright; ++k) {
                    final double xk   = xval[k];
                    final double yk   = yval[k];
                    final double dist = (k < i) ? x - xk : xk - x;
                    final double w    = tricube(dist * denom) * robustnessWeights[k] * weights[k];
                    final double xkw  = xk * w;
                    sumWeights += w;
                    sumX += xkw;
                    sumXSquared += xk * xkw;
                    sumY += yk * w;
                    sumXY += yk * xkw;
                }

                final double meanX = sumX / sumWeights;
                final double meanY = sumY / sumWeights;
                final double meanXY = sumXY / sumWeights;
                final double meanXSquared = sumXSquared / sumWeights;

                final double beta;
                if (JdkMath.sqrt(JdkMath.abs(meanXSquared - meanX * meanX)) < accuracy) {
                    beta = 0;
                } else {
                    beta = (meanXY - meanX * meanY) / (meanXSquared - meanX * meanX);
                }

                final double alpha = meanY - beta * meanX;

                res[i] = beta * x + alpha;
                residuals[i] = JdkMath.abs(yval[i] - res[i]);
            }

            // No need to recompute the robustness weights at the last
            // iteration, they won't be needed anymore
            if (iter == robustnessIters) {
                break;
            }

            // Recompute the robustness weights.

            // Find the median residual.
            // An arraycopy and a sort are completely tractable here,
            // because the preceding loop is a lot more expensive
            System.arraycopy(residuals, 0, sortedResiduals, 0, n);
            Arrays.sort(sortedResiduals);
            final double medianResidual = sortedResiduals[n / 2];

            if (JdkMath.abs(medianResidual) < accuracy) {
                break;
            }

            for (int i = 0; i < n; ++i) {
                final double arg = residuals[i] / (6 * medianResidual);
                if (arg >= 1) {
                    robustnessWeights[i] = 0;
                } else {
                    final double w = 1 - arg * arg;
                    robustnessWeights[i] = w * w;
                }
            }
        }

        return res;
    }

    /**
     * Compute a loess fit on the data at the original abscissae.
     *
     * @param xval the arguments for the interpolation points
     * @param yval the values for the interpolation points
     * @return values of the loess fit at corresponding original abscissae
     * @throws NonMonotonicSequenceException if {@code xval} not sorted in
     * strictly increasing order.
     * @throws DimensionMismatchException if {@code xval} and {@code yval} have
     * different sizes.
     * @throws NoDataException if {@code xval} or {@code yval} has zero size.
     * @throws NotFiniteNumberException if any of the arguments and values are
     * not finite real numbers.
     * @throws NumberIsTooSmallException if the bandwidth is too small to
     * accommodate the size of the input data (i.e. the bandwidth must be
     * larger than 2/n).
     */
    public final double[] smooth(final double[] xval, final double[] yval)
        throws NonMonotonicSequenceException,
               DimensionMismatchException,
               NoDataException,
               NotFiniteNumberException,
               NumberIsTooSmallException {
        if (xval.length != yval.length) {
            throw new DimensionMismatchException(xval.length, yval.length);
        }

        final double[] unitWeights = new double[xval.length];
        Arrays.fill(unitWeights, 1.0);

        return smooth(xval, yval, unitWeights);
    }

    /**
     * Given an index interval into xval that embraces a certain number of
     * points closest to {@code xval[i-1]}, update the interval so that it
     * embraces the same number of points closest to {@code xval[i]},
     * ignoring zero weights.
     *
     * @param xval Arguments array.
     * @param weights Weights array.
     * @param i Index around which the new interval should be computed.
     * @param bandwidthInterval a two-element array {left, right} such that:
     * {@code (left==0 or xval[i] - xval[left-1] > xval[right] - xval[i])}
     * and
     * {@code (right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left])}.
     * The array will be updated.
     */
    private static void updateBandwidthInterval(final double[] xval,
                                                final double[] weights,
                                                final int i,
                                                final int[] bandwidthInterval) {
        final int left = bandwidthInterval[0];
        final int right = bandwidthInterval[1];

        // The right edge should be adjusted if the next point to the right
        // is closer to xval[i] than the leftmost point of the current interval
        int nextRight = nextNonzero(weights, right);
        int nextLeft = left;
        while (nextRight < xval.length &&
               xval[nextRight] - xval[i] < xval[i] - xval[nextLeft]) {
            nextLeft = nextNonzero(weights, bandwidthInterval[0]);
            bandwidthInterval[0] = nextLeft;
            bandwidthInterval[1] = nextRight;
            nextRight = nextNonzero(weights, nextRight);
        }
    }

    /**
     * Return the smallest index {@code j} such that
     * {@code j > i && (j == weights.length || weights[j] != 0)}.
     *
     * @param weights Weights array.
     * @param i Index from which to start search.
     * @return the smallest compliant index.
     */
    private static int nextNonzero(final double[] weights, final int i) {
        int j = i + 1;
        while(j < weights.length && weights[j] == 0) {
            ++j;
        }
        return j;
    }

    /**
     * Compute the
     * <a href="http://en.wikipedia.org/wiki/Local_regression#Weight_function">tricube</a>
     * weight function.
     *
     * @param x Argument.
     * @return <code>(1 - |x|<sup>3</sup>)<sup>3</sup></code> for |x| &lt; 1, 0 otherwise.
     */
    private static double tricube(final double x) {
        final double absX = JdkMath.abs(x);
        if (absX >= 1.0) {
            return 0.0;
        }
        final double tmp = 1 - absX * absX * absX;
        return tmp * tmp * tmp;
    }
}